Energy spectra, angular spread, fluence profiles and dose distributions of 6 and 18 MV photon beams: results of Monte Carlo simulations for a Varian 2100EX accelerator
Bibliographic record
Abstract
The purpose of this study is to provide detailed characteristics of incident photon beams for different field sizes and beam energies. This information is critical to the future development of accurate treatment planning systems. It also enhances our knowledge of radiotherapy photon beams. The EGS4 Monte Carlo code, BEAM, has been used to simulate 6 and 18 MV photon beams from a Varian Clinac-2100EX accelerator. A simulated realistic beam is stored in a phase space data file, which contains details of each particle's complete history including where it has been and where it has interacted. The phase space files are analysed to obtain energy spectra, angular distribution, fluence profile and mean energy profiles at the phantom surface for particles separated according to their charge and history. The accuracy of a simulated beam is validated by the excellent agreement between the Monte Carlo calculated and measured dose distributions. Measured depth-dose curves are obtained from depth-ionization curves by accounting for newly introduced chamber fluence corrections and the stopping-power ratios for realistic beams. The study presents calculated depth-dose components from different particles as well as calculated surface dose and contribution from different particles to surface dose across the field. It is shown that the increase of surface dose with the increase of the field size is mainly due to the increase of incident contaminant charged particles. At 6 MV, the incident charged particles contribute 7% to 21% of maximum dose at the surface when the field size increases from 10 x 10 to 40 x 40 cm2. At 18 MV, their contributions are up to 11% and 29% of maximum dose at the surface for 10 x 10 cm2 and 40 x 40 cm2 fields respectively. However, the fluence of these incident charged particles is less than 1% of incident photon fluence in all cases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".